This is the documentation for Recommendations AI only.

To see documentation for Recommendations AI, Retail Search, and the unified Retail console (which is applicable to both Recommendations AI and Retail Search users), go to the How-to guides for the Retail API and the API reference documentation for the Retail API. If you are using the v1beta version of Recommendations AI, migrate to the GA version: Migrating to the Retail API from beta.

Features and capabilities of Recommendations AI

Recommendations AI enables you to build high quality personalized product recommendation systems without requiring a high level of expertise in machine learning, systems design, or operations. Leveraging your website's catalog products and user behavior, Recommendations AI builds a recommendation model specific to your company. You can then request recommendations for other catalog products to display to your users.

The Retail API uses user events and your product catalog to train recommendation machine learning models, which provide recommendations based on this data.

Recommendations AI capabilities include:

  • Custom models. Each model is trained specifically for your data, based on machine learning, sequence, and transformer techniques.

  • Personalized results. Leverage personalization algorithms without any machine learning expertise. Recommendations are based on user behavior and activities like views, clicks, and in-store purchases as well as online activity, so that every prediction result is personalized.

  • Real-time predictions. Each retrieved recommendation considers previous user activity like click, view, and purchase events, so recommendations are in real time.

  • Automatic model training and tuning. Daily model retraining ensures all the models can accurately capture user behavior every day.

  • Optimization objectives. Goals like conversion rate, click-through rate, and revenue optimization help you precisely optimize for your business goal.

  • Omnichannel recommendations. With the API model, go beyond website recommendations to personalize your entire shopper journey to recommendations on mobile apps, personalized email recommendations, store kiosks, or call center applications.

Using the Retail API

In order to build recommendation machine learning models, Recommendations AI needs two sets of information:

  • Product catalog: Information of the products sold to customers. This includes the product title, description, in stock availability, pricing, and so on.

  • User events: End user behavior on your website. This includes users searching for, viewing, or purchasing a specific item, your website showing users a list of products, and so on.

Using the Retail API

To build machine learning models for recommendations, Recommendations AI needs two sets of information:

  • Product catalog: Information of the products sold to customers. This includes the product title, description, in-stock availability, and pricing.

  • User events: End user behavior on your website. This includes users viewing or purchasing a specific item, your website showing users a list of products, and so on.

With many integration options, you can ingest your data using tools you might already use, such as BigQuery, Cloud Storage, Merchant Center, Tag Manager, and Google Analytics.